The European Extreme Events Climate Index

What is the European Extreme Events Climate Index

The European Extreme Events Climate Index (E3CI) is an ensemble of indices aimed at providing information about the areas affected by different types of weather-induced hazards and the severity of such events. Currently, based on the corresponding index developed for North America (Actuaries Climate Index, ACI; actuariesclimateindex.org), E3CI includes five components returning information about main hazards: cold and heat stresses, droughts, extreme precipitations, extreme winds. Moreover, these components are combined into a single index, providing a general overview of the hazards. The data will be made available on different Administrative unit levels; currently, data is released at Country level.

See the variations by country and by component

Download E3CI data selecting the area and the period of interest

SPOTLIGHTS

Read our focus on extreme atmospheric events

E3CI components

The assessment of the components exploits ERA5 (doi:10.24381/cds.adbb2d47), the fifth-generation atmospheric reanalysis produced by European Centre for Medium-Range Weather Forecasts [ECMWF] (https://youtu.be/FAGobvUGl24). ERA5 covers the entire Globe on regular latitude-longitude grids at 0.25° x 0.25° resolution from January 1950 to present. Hourly data on many atmospheric parameters together with estimates of uncertainty are available on Climate Data Store of Copernicus Climate Change Service [C3S]. ERA5 is updated daily with a latency of about 5 days permitting a constant update of the components forming E3CI.

Each component uses an indicator as proxy for several hazards. The reference value is computed on 1981-2010 time span while, at monthly basis, E3CI shows a standardized anomaly with respect to the reference values.

Disclaimer:
E3CI data is generated using Copernicus Climate Change Service information [2021]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

The E3CI components are defined as follows:

i) HEAT STRESS

BASELINE CALCULATION: On the reference period 1981-2010, for each calendar day, the maximum temperature of the surrounding five days is considered. The 95th percentile among the 150 values (5 days x 30 years) is computed and assumed as threshold. For each month j, the mean value \mu\left(j,\ T_{max}\right)\ and the standard deviation \sigma\left(j,\ T_{max}\right)\ of the number of days exceeding the corresponding threshold are calculated.

STANDARDIZED ANOMALY COMPUTATION: Each month j and year k the number of days exceeding the corresponding threshold {HS}_{j,k} is standardized according to the formula:

{HS}_{std,j,k}=\frac{{HS}_{j,k}-\mu\left(j,\ T_{max}\right)}{\sigma\left(j,\ T_{max}\right)}

ii) COLD STRESS

BASELINE CALCULATION: On the reference period 1981-2010, for each calendar day, the minimum temperature of the surrounding five days is considered. The 5th percentile among the 150 values (5 days x 30 years) is computed and assumed as threshold. For each month j, the mean value \mu\left(j,\ T_{min}\right)\ and the standard deviation \sigma\left(j,\ T_{min}\right)\ of the number of days lower than the corresponding threshold are calculated.

STANDARDIZED ANOMALY COMPUTATION: Each month j and year k the number of days lower than the corresponding threshold {CS}_{j,k} is standardized according to the formula:

{CS}_{std,j,k}=\frac{{CS}_{j,k}-\mu\left(j,\ T_{min}\right)}{\sigma\left(j,\ T_{min}\right)}

iii) DROUGHT

BASELINE CALCULATION: Standard Precipitation Index (SPI) is assumed as reference indicator considering 3 months as accumulation period of interest (SPI-3). Over 1981-2010, for each month j the 30 cumulated values are fitted to a gamma probability distribution which is then transformed into a normal distribution.

STANDARDIZED ANOMALY COMPUTATION: For each month j and year {SPI-3}_{j,k} value represents units of standard deviation from the long-term reference mean. According to the canonical approach, positive SPI indicate values greater than median precipitation and negative values indicate less than median precipitation. In E3CI, to maintain the consistency with the other components, the opposite of {SPI-3}_{j,k} is taken.

iv) EXTREME PRECIPITATION

BASELINE CALCULATION: On the reference period 1981-2010, for each month j, the 95th percentile of daily precipitation is computed. Then, the exceedance value at monthly basis is computed as:

{EP}_{j,k}=\sum_{i=1}^{n_j}{\max{\left[0;\ P_{i,j,k}-P_{95,j}\right]}\ }

where P_{i,j,k} represents the daily precipitation (day i, month j, year k

Over the reference period, for each month j, the mean value \mu\left(\ {EP}_j\right)\ and the standard deviation \sigma\left(\ {EP}_j\right)\ of the exceedance value are calculated.

STANDARDIZED ANOMALY COMPUTATION: Each month j and year k the exceedance value is standardized according to the formula:

{EP}_{std,j,k}=\frac{{EP}_{j,k}-\mu\left({EP}_j\right)}{\sigma\left(\ {EP}_j\right)}

v) EXTREME WIND

BASELINE CALCULATION: On the reference period 1981-2010, for each month j, the 95th percentile of daily maximum wind speed is computed, w_{95,j}. Then, at monthly basis, Local Loss Index (LLI, Donat et al., 2011; doi:10.5194/nhess-11-1351-2011) is calculated as:

{LLI}_{j,k}=\sum_{i=1}^{n_j}max\left[0;\left(\frac{w_{max,i,j,k}}{w_{95,j}}-1\right)^3\right]

Where w_{max,i,j,k} is the maximum wind speed computed considering mean hourly values. Over the reference period, for each month j, the mean value \mu\left(\ {LLI}_j\right)\ and the standard deviation \sigma\left({LLI}_j\right)\ are calculated.

STANDARDIZED ANOMALY COMPUTATION: Each month j and year k, the exceedance value is standardized according to the formula:

{LLI}_{std,j,k}=\frac{{LLI}_{j,k}-\mu\left(\ {EP}_j\right)}{\sigma\left(\ {EP}_j\right)}

vi) E3CI

For each month j and year k, the European Extreme Events Climate Index is given by the mean of the different components. Different and more effective formulations are currently under development.

While E3CI applications range in a wide area comprising both scientific and technical use cases, E3CI has been derived mainly to link the occurrences of relevant weather events to their impacts, particularly in terms of economic and insurance losses. Such an information can be useful to adjust rate adequacy levels, improve budgeting capabilites and risk management, designing index-linked financial instruments and new insurance and re-insurance products.

Finally, E3CI can be adopted to pursue education and sustainability targets, as delivering awareness about climate change, support public decision making processes for sustainable development, provide an objective measure to monitor climate trends.

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